Scale-invariance and patchiness in the plankton
Bibliographic record
Abstract
An 'in-situ' oceanographic CTD probe linked to an Optical Plankton Counter was used to produce a collection of transects in the nearshore region of the Gulf of St. Lawrence estuary near Rimouski, Quebec. Gap relationships at the millimeter-meter scale, using a Distance to Next Encounter (DNE) indicated that zooplankton were significantly aggregated into patches and randomly distributed within patches and new statistics were used to describe 'in-situ' patchiness. Zooplankton distributions were compared with the CTD data (salinity, oxygen, temperature, optical transitivity, and phytoplankton as fluorescence). Spectral analyses indicated that the plankton spectra (variance as a function of frequency scale) were different from a "passive scaler" (temperature), which had only one scaling region with slope [beta]~5/3 (Kolmogorov turbulent value). Zooplankton had 2 scaling regions: >300m with [beta]~5/3 and <300m with a [beta]~0. Phytoplankton had 3 scaling regions: >300m and <40m which had [beta]~5/3 with an intermediate scale (40-300m) with [beta]~0. Multifractal analysis indicated both zooplankton and phytoplankton were extremely multifractal ("spiky") with [alpha]~1.8-1.9, but with low C1~0.05 indicating mean values were common. A simple model is presented involving growth and turbulence to account for the large-scale, and grazing and turbulence (predator-prey zooplankton/phytoplankton interactions) to account for the small-scale (particularly H, related to [beta]). Depending on a dimensionless grazing constant, small scales are dominated by turbulent grazing (Gr > 1) or passive-scalar turbulence (Gr < 1). Within the grazing regime, H = -1/3, zooplankton preferentially graze the high concentration phytoplankton patches. Multifractal analysis of zooplankton indicated a strong similarity with phytoplankton multifractal parameters for scales 300-40 m, but phytoplankton were otherwise passive-scalers, indicating zooplankton modify the distribution of phytoplankton at these scales. The multifractal parameters: [alpha] and C1 provide a full description of a complex field to high statistical moments. Ranges of realistic patchy food fields were simulated using the multifractal process. Capture success of planktonic copepods was then investigated in an individual-based model. Average capture rates declined with increased patchiness, but individual capture rates were higher at increased patchiness, then becoming increasingly log-normal as most individuals captured no food.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".